Why configure code checking tools using configuration files?

Configuration files give you the ability to declare your project's preferred configuration and distribute it to all participants in the project. It smooths out the practice of data science (and software development too), as these configuration represent the declared normative state of a project, answering questions such as:

• What code style rules ought we adhere to?
• How much docstring coverage ought to be present?
• What software tests should be run?

Without these configuration files declaring how code checkers ought to behave, we leave it up to collaborators and contributors to manually configure their local systems, and without sufficient documentation, they may bug you over and over on how things ought to be configured. This increase in friction will inevitably lead to an increase in frustration with the project, and hence a decrease in engagement.

As such, you can think of these configuration files as part of your automation toolkit, thus satisfying the automation philosophy.

What configuration files belong with which code checking tools?

Because configuration files are so crucial to a project, I have collated them together on the Configuration file overview page.

When do I create these configuration files?

As always, just-in-time, at the moment that you need it.

Treat your projects as if they were software projects for maximum organizational effectiveness. Why? The biggest reason is that it will nudge us towards getting organized. The "magic" behind well-constructed software projects is that someone sat down and thought clearly about how to organize things. The same principle can be applied to data analysis projects.

Firstly, some overall ideas to ground the specifics:

• Follow the rule of one-to-one in managing your projects
• Sanely name things consistently

Some ideas pertaining to Git:

• One project should get one git repository
• Set up an awesome default gitignore for your projects
• Adhere to best git practices
• Set up pre-commit hooks to automate checks before making git commits

Notes that pertain to organizing files:

• Set up your project with a sane directory structure
• Place custom source code inside a lightweight package
• Keep your notebooks organized with logical categories

Notes that pertain to your compute environment:

• Create one conda environment per project
• Create runtime environment variable configuration files for each of your projects
• Use scripts to automate routine execution of tasks
• See the Configuration file overview

And notes that pertain to good coding practices:

• Write effective documentation for your projects
• Install code checking tools to help write better code
• Define project-wide constants inside your custom package
• Write tests that test your custom code
• Build a continuous integration pipeline for your source
• Use pyprojroot to define relative paths to the project root
• Create configuration files for code checking tools

Treating projects as if they were software projects, but without software engineering's stricter practices, keeps us primed to think about the generalizability of what we do, but without the over-engineering that might constrain future flexibility.